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My experience with LLm-based chat is so different from what the article (and some friends) describe. I use LLM chat for a wide range of tasks including coding,

My experience with LLm-based chat is so different from what the article (and some friends) describe. I use LLM chat for a wide range of tasks including coding, writing, brainstorming, learning, etc. It’s mostly right enough. And so my usage of it has only increased and expanded. I don’t know how less right it needs to be or how often to reduce my usage. Honestly, I think it’s hard to change habits and LLM chat, at its most useful, is attempting to replace decades long habits. Doesn’t mean quality evaluation is bad. It’s what got us where we are today and what will help us get further. My experience is anecdotal. But I see this divide in nearly all discussions about LLM usage and adoption.

bluefirebrand 1y ago
> It’s mostly right enough. Honestly this is why your experience is different: your expectations are different (and likely lower). I never find they are "mostly right enough", I find they are "mostly wrong in ways that range from subtle mistakes to extremely incorrect". The more subtly they are wrong, the worse I rate their output actually, because that is what costs me more time when I try to use them I want tools that save me time. When I use LLMs I have to carefully write the prompts, read and understand, evaluate, and iterate on the output to get "close enough" then fix it up to be actually correct. By the time I've done all of that, I probably could have just written it from scratch. The fact is that typing speed has basically never been the bottleneck for developer productivity, and LLMs basically don't offer much except "generate the lines of code more quickly" imo
- mjr00 1y ago
  It's also what you're writing. The GP's commenter's bio shows they're a product lead, not a full-time software developer. To make some broad assumptions about what kind of code they're talking about: using an LLM for "write me a Python script that queries the Jira API for all tickets closed in the past week" is a much different task from "change the code in our 15 year old in-house accounting software to handle these tariffs", both in terms of the code that gets written as well as the consequences of the LLM getting it wrong. To be clear this isn't a knock on anyone's work, but it does seem to be a source of why "pro-LLM" and "anti-LLM" groups tend to talk past each other.
  - bluefirebrand 1y ago
    Sure, but in both cases you are running a real risk of producing incorrect data If you're a product lead and you ask an LLM to produce a script that gets that output, you still should verify the output is correct Otherwise you run a real risk of seeming like an idiot later when you give a report on "tickets closed in the past week" and your data is completely wrong. "Why hasn't John closed any tickets this week? Is he slacking off?"... "What he closed more tickets than anyone..." And then it turns out that the unreliable LLM script excluded him for whatever reason Of course I understand that people are not going to actually be this careful, because more and more people are trusting LLM output without verifying it. Because it's "right enough" that we are becoming complacent
    - keybrd-intrrpt 1y ago
      > you still should verify the output is correct And that's a problem with the workflow, not a problem with the LLM. It's no different than verifying the information from your Google search or the Stack Overflow answer you found works. But for some reason there are people that have higher expectations of LLM output.
      - bluefirebrand 1y ago
        People aren't trying to produce entire codebases in 10 minutes using Stack Overflow, or giving it free reign to refactor the entire codebase
    - mjr00 1y ago
      You're absolutely right. You need to verify the script works, and you need to be able to read the code to see what it's actually doing and if it passes the smell test (as a sibling commenter said, the same way you would for a code snippet off StackOverflow). But ultimately for these bits which are largely rote "take data from API, transform into data format X" tasks, LLMs do a great job getting at least 95% of the way there, in my experience. In a lot of ways they're the perfect job for LLMs: most of the work is just typing (as in, pressing buttons on a keyboard) and passing the right arguments to an API, so why not outsource that to an LLM and verify the output? The challenge comes when dealing with larger systems. Like an LLM might suggest Library A for accomplishing a task, but if your codebase already has Library B for that already, or maybe Library A but a version from 2020 with a different API, you need to make judgment calls about the right approach to take, and the LLM can't help you there. Same with code style, architecture, how future-proof-but-possibly-YAGNI you want your design to be, etc. I don't think "vibe coding" or making large changes across big code bases really works (or will ever really work), but I do think LLMs are useful for isolated tasks and it's a mistake to totally dismiss them.
- throwacct 1y ago
  This. I use LLMs for some tasks, but for more complex issues, I do it myself. I tried to use it for a project by defining each task as clearly as possible, and I spent weeks trying to come up with something useful. Mind you, I achieved 80% of what I wanted after iterating and "telling" the chat that their answers were wrong, and going over the code to double-check if everything was okay. Now I use it for specific, simple tasks if these are work-related, and then use it for random kinds of stuff that I can verify by going to the actual source.
  - bluefirebrand 1y ago
    > Mind you, I achieved 80% of what I wanted after iterating and "telling" the chat that their answers were wrong, and going over the code to double-check if everything was okay I very often read things like this, and I'm surprised how often the person estimates "around 80%" of the work was good. It feels so perfectly tailored to the Pareto Principal The LLM does the easy 80% (which we usually say takes 20% of the time anyways). Then the human has to go do the harder remaining 20%, only with a much smaller mental model of how the original 80% is fitting together
- empath75 1y ago
  They save me a tremendous amount of time, you just need to be smart about what you try to get them to do. _Busy work_ is what you want to focus on, not anything that takes a ton of domain knowledge and intelligence. Just as an example from today, i had a huge pile of yaml documents that needed to have some transformations done to them -- they were pretty simple and obvious, but I just went into cursor, give it a before and after and a few notes, and it wrote a python script in less than 10 seconds that converted everything exactly the way I needed. Did it save me a day of work? Probably not, but probably an hour or so of looking up python docs and iterating until i worked out all the syntax errors myself? An hour here and an hour there adds up to a _lot_ of saved time. I spent more time just writing this comment then I did asking cursor to write and run that script for me. Other things I had an LLM do for me just _today_ is fix a github action that was failing, and knock out a developer readme for a helm chart documenting what all the values do -- that's one of the kinds of things where it gets a lot of stuff wrong, but typing speed _is_ the bottleneck. It took me a minute or so to fix the stuff it misunderstood, but the formatting and the bulk of it was fine.
  - bluefirebrand 1y ago
    > _Busy work_ is what you want to focus on, not anything that takes a ton of domain knowledge and intelligence Eh.. Maybe that's more of a sign that we shouldn't be doing busywork in the first place
    - empath75 1y ago
      You are in a magical place if you never have to do busy work.
  - bgnn 1y ago
    Isn't the article saying it's mainly useful for SW? I'm an electrical engineer and the only cases LLMs useful were developing phyton scripts or translating a text into a foreign language that I'm fluently speaking. They are absolutely garbage for anything electrical engineering related, even coding RTL.
strangattractor 1y ago
IMHO it's a great summarizing search engine. I now don't have to click on a link to go to that original source - Gemini just hands me a useful summary. Ask AI to do something specific that requires GI (General Intelligence) your milage may vary. So as OpenAI and Google suck in all your content (creators) you are going to find yourself derive less and less revenue generated by visits to your site. Just sayin.
- hooverd 1y ago
  DuckDuckGo, which uses Bing I think, now has Bing's AI summaries instead of the goddamn content in search results, which makes evaluating the search results at a glance useless!
  - yegg 1y ago
    For what it's worth, we produce our own summaries, and you can turn them off if you don't like them. We also offer noai.duckduckgo.com, which turns all of our AI features off automatically.
- foobiekr 1y ago
  Gemini routinely inaccurately reports the contents in the summary. I have found it actually reversing things on a regular basis. The summary says no and the source says yes.
nomel 1y ago
From what I can tell, rather than a simple difference in expectation (which could explain your positive experience vs others), it seems to be a "comfort within uncertainty" difference that, from what I can tell, is a personality trait! You're comfortable with the uncertainty, and accommodate it in your use and expectations. You're left feeling good about the experience, within that uncertainty. Others are repelled by uncertainty, so will have a negative experience, regardless of how well it may work for a subset of tasks they try, because that repulsive uncertainty is always present. I think it would be interesting (and possibly very useful/profitable for the marketing/UI departments of companies that use AI) to find the relation between perceived AI usefulness and the results of some of the "standard" personality tests.
- kenjackson 1y ago
  I wonder if this is like dishwasher usage. As a kid growing up we never used the dishwasher. It was just the drying rack. The reason was you had to rinse off the big stuff anyways, and then the resulting quality of dishwashing was poor in it. You'd often get a fork with rice stuck between it still, which was unacceptable. As a grown up now I use a dishwasher for everything that is permitted to go in it. I still have to rinse off plates first, and occasionally I do see rice between a fork that I have to then clean manually. But I'm not comfortable knowing that it won't clean as well as I could by hand, but it does a good enough job -- and in some ways a much better job (it uses much hotter water than I do by hand). I don't know if my mom could ever really be comfortable with it though.
  - bluefirebrand 1y ago
    Imagine if the advice for Dishwasher usage mirrored the advice for AI "You have to iterate on the output to get good results" Just keep running that dishwasher until they're clean! If you run it and they're still dirty, load it up with soap and try again!
    - keybrd-intrrpt 1y ago
      That's all new technology though. Dishwashers _were_ like that. What's seemed to change are people's expectations of technology that "just works". When in reality, we are in the infant years of AI/ML and LLMs We're so spoiled by the pace of innovation we're upset it requires a bit of hand-holding while they figure things out.
leptons 1y ago
It's fine if LLMs are used casually, for things that don't affect anyone but the user. But when someone plugs an LLM into Social Security or other governmental bodies to take action on real human beings, then disaster awaits. Nobody is going to care if the LLM got it wrong if you're just chatting with it or writing some wonky code that doesn't matter in the real world, but when your government check is reduced or deleted by an LLM that is hallucinating, then the real problems start. These things should not be trusted with anything but the least consequential actions an individual would use it for.
- gte525u 1y ago
  ^This - we're trying to use one to partially automate some system engineering type activities. It's great for reviews where any given reviewer could be expected to have a misunderstanding of certain details or skip a section (RAG somewhat helps this) - but it's frustrating for artifact generation where missing details cascade through the project. As great as the technology (right now) it seems so far from reliable business process automation.
light_hue_1 1y ago
> It’s mostly right enough What do you use it for? In my space, "mostly right enough" isn't useful. Particularly when that means that the errors are subtle and I might miss them. I can't write whitepapers that tell people to do things that would result in major losses.
fellowniusmonk 1y ago
I really don't understand people who are down on LLM. In terms of code output. I have gone from the productivity of being a Sr. Engineer to a team with .8 of a Sr. Engineer, 5 Jr. Engineers and One dude solely dedicated to reading/creating documentation. Unlike a lot of my fellow engineers who are also from traditional CS backgrounds and haven't worked in revenue restricted startup environments, I also have been VERY into interpreted languages like ruby in the past. Now compiled languages are even better, I think from a velocity perspective compiled languages are now incredibly on par for prototyping velocity and have had their last weakness removed. It's both exciting and scary, I can't believe how people are still sleep walking in this environment and don't realize we are in a different world. Once again the human inability to "gut reason" about exponentials is going to screw us all over. One terribly overlooked thing I've noticed that I think explains the differing takes. Foundation of my position here: https://www.nature.com/articles/s41598-020-60661-8 https://www.nature.com/articles/s41598-020-60661-8 Within the population that writes code there are a small number of successful people who approach the topic in a ~purely mathematical approach, and a small number of successful people that approach writing code in a ~purely linguistic approach. Most people fall somewhere in the middle. Those who are on the MOST extreme end of the mathematic side and are linguistically bereft HATE LLM's and effectively cannot use them. My guess is that HN population will tend to show stronger reactions against LLM's because it was heavily seeded with functional programmers which I think has a concentration of the successful extremely math focused. I worked for several years in a purely functional shop and that was my observation: Elixir, Haskell, Ramda. Just my speculation.
- whyowhy3484939 1y ago
  There is this interesting thing called the Paradox of Automation where increasing automation increases the importance of human intervention. We are trying this out on a societal level. It will be.. interesting, to say the least. Also, congratulations on becoming a team. I sure hope you have the mental bandwidth to check all that output carefully. If so, doubly congrats, because you might be the smartest human that ever lived.
  - fellowniusmonk 1y ago
foobiekr 1y ago
Charitably, your low expectations are probably the source of your finding them acceptable. It’s also possible - and you should not take this as an insult, it’s just the way it is - you may not know enough about the subjects of your interactions to really spot how wrong they are. However the cases you list - brainstorming - don’t really care about wrong answers. Coding is in the eye of the beholder, but for anything that isn’t junk glue code, scripts or low-complexity web stuff, I find the output of LLMs just short of horrendous.
- CuriouslyC 1y ago
  The code that the best frontier models produce is definitely good if you prompt it with what you believe "good" means, with the caveat that code quality depends heavily on the language -- Python, Typescript/Javascript, Java and C are quite good, Rust, C++ and Go tend to be decent to weak depending on the specific model, and other languages are poor.
  - fellowniusmonk 1y ago
    Yeah, I've had mixed results with Rust. Oddly it's been most helpful for me so far in getting Rust code running in WASM without having to know anything about WASM, which I have found delightful.
  - foobiekr 1y ago
    The C output is absolutely terrible. I cannot fathom an experienced C coder who has found otherwise for anything non trivial. The code is full of things like return from stack, poor buffer size discipline, etc.

bluefirebrand 1y ago

> It’s mostly right enough. Honestly this is why your experience is different: your expectations are different (and likely lower). I never find they are "mostly right enough", I find they are "mostly wrong in ways that range from subtle mistakes to extremely incorrect". The more subtly they are wrong, the worse I rate their output actually, because that is what costs me more time when I try to use them I want tools that save me time. When I use LLMs I have to carefully write the prompts, read and understand, evaluate, and iterate on the output to get "close enough" then fix it up to be actually correct. By the time I've done all of that, I probably could have just written it from scratch. The fact is that typing speed has basically never been the bottleneck for developer productivity, and LLMs basically don't offer much except "generate the lines of code more quickly" imo

mjr00 1y ago
It's also what you're writing. The GP's commenter's bio shows they're a product lead, not a full-time software developer. To make some broad assumptions about what kind of code they're talking about: using an LLM for "write me a Python script that queries the Jira API for all tickets closed in the past week" is a much different task from "change the code in our 15 year old in-house accounting software to handle these tariffs", both in terms of the code that gets written as well as the consequences of the LLM getting it wrong. To be clear this isn't a knock on anyone's work, but it does seem to be a source of why "pro-LLM" and "anti-LLM" groups tend to talk past each other.
- bluefirebrand 1y ago
  Sure, but in both cases you are running a real risk of producing incorrect data If you're a product lead and you ask an LLM to produce a script that gets that output, you still should verify the output is correct Otherwise you run a real risk of seeming like an idiot later when you give a report on "tickets closed in the past week" and your data is completely wrong. "Why hasn't John closed any tickets this week? Is he slacking off?"... "What he closed more tickets than anyone..." And then it turns out that the unreliable LLM script excluded him for whatever reason Of course I understand that people are not going to actually be this careful, because more and more people are trusting LLM output without verifying it. Because it's "right enough" that we are becoming complacent
  - keybrd-intrrpt 1y ago
    > you still should verify the output is correct And that's a problem with the workflow, not a problem with the LLM. It's no different than verifying the information from your Google search or the Stack Overflow answer you found works. But for some reason there are people that have higher expectations of LLM output.
    - bluefirebrand 1y ago
      People aren't trying to produce entire codebases in 10 minutes using Stack Overflow, or giving it free reign to refactor the entire codebase
  - mjr00 1y ago
    You're absolutely right. You need to verify the script works, and you need to be able to read the code to see what it's actually doing and if it passes the smell test (as a sibling commenter said, the same way you would for a code snippet off StackOverflow). But ultimately for these bits which are largely rote "take data from API, transform into data format X" tasks, LLMs do a great job getting at least 95% of the way there, in my experience. In a lot of ways they're the perfect job for LLMs: most of the work is just typing (as in, pressing buttons on a keyboard) and passing the right arguments to an API, so why not outsource that to an LLM and verify the output? The challenge comes when dealing with larger systems. Like an LLM might suggest Library A for accomplishing a task, but if your codebase already has Library B for that already, or maybe Library A but a version from 2020 with a different API, you need to make judgment calls about the right approach to take, and the LLM can't help you there. Same with code style, architecture, how future-proof-but-possibly-YAGNI you want your design to be, etc. I don't think "vibe coding" or making large changes across big code bases really works (or will ever really work), but I do think LLMs are useful for isolated tasks and it's a mistake to totally dismiss them.
throwacct 1y ago
This. I use LLMs for some tasks, but for more complex issues, I do it myself. I tried to use it for a project by defining each task as clearly as possible, and I spent weeks trying to come up with something useful. Mind you, I achieved 80% of what I wanted after iterating and "telling" the chat that their answers were wrong, and going over the code to double-check if everything was okay. Now I use it for specific, simple tasks if these are work-related, and then use it for random kinds of stuff that I can verify by going to the actual source.
- bluefirebrand 1y ago
  > Mind you, I achieved 80% of what I wanted after iterating and "telling" the chat that their answers were wrong, and going over the code to double-check if everything was okay I very often read things like this, and I'm surprised how often the person estimates "around 80%" of the work was good. It feels so perfectly tailored to the Pareto Principal The LLM does the easy 80% (which we usually say takes 20% of the time anyways). Then the human has to go do the harder remaining 20%, only with a much smaller mental model of how the original 80% is fitting together
empath75 1y ago
They save me a tremendous amount of time, you just need to be smart about what you try to get them to do. _Busy work_ is what you want to focus on, not anything that takes a ton of domain knowledge and intelligence. Just as an example from today, i had a huge pile of yaml documents that needed to have some transformations done to them -- they were pretty simple and obvious, but I just went into cursor, give it a before and after and a few notes, and it wrote a python script in less than 10 seconds that converted everything exactly the way I needed. Did it save me a day of work? Probably not, but probably an hour or so of looking up python docs and iterating until i worked out all the syntax errors myself? An hour here and an hour there adds up to a _lot_ of saved time. I spent more time just writing this comment then I did asking cursor to write and run that script for me. Other things I had an LLM do for me just _today_ is fix a github action that was failing, and knock out a developer readme for a helm chart documenting what all the values do -- that's one of the kinds of things where it gets a lot of stuff wrong, but typing speed _is_ the bottleneck. It took me a minute or so to fix the stuff it misunderstood, but the formatting and the bulk of it was fine.
- bluefirebrand 1y ago
  > _Busy work_ is what you want to focus on, not anything that takes a ton of domain knowledge and intelligence Eh.. Maybe that's more of a sign that we shouldn't be doing busywork in the first place
  - empath75 1y ago
    You are in a magical place if you never have to do busy work.
- bgnn 1y ago
  Isn't the article saying it's mainly useful for SW? I'm an electrical engineer and the only cases LLMs useful were developing phyton scripts or translating a text into a foreign language that I'm fluently speaking. They are absolutely garbage for anything electrical engineering related, even coding RTL.

strangattractor 1y ago

IMHO it's a great summarizing search engine. I now don't have to click on a link to go to that original source - Gemini just hands me a useful summary. Ask AI to do something specific that requires GI (General Intelligence) your milage may vary. So as OpenAI and Google suck in all your content (creators) you are going to find yourself derive less and less revenue generated by visits to your site. Just sayin.

hooverd 1y ago
DuckDuckGo, which uses Bing I think, now has Bing's AI summaries instead of the goddamn content in search results, which makes evaluating the search results at a glance useless!
- yegg 1y ago
  For what it's worth, we produce our own summaries, and you can turn them off if you don't like them. We also offer noai.duckduckgo.com, which turns all of our AI features off automatically.
foobiekr 1y ago
Gemini routinely inaccurately reports the contents in the summary. I have found it actually reversing things on a regular basis. The summary says no and the source says yes.

nomel 1y ago

From what I can tell, rather than a simple difference in expectation (which could explain your positive experience vs others), it seems to be a "comfort within uncertainty" difference that, from what I can tell, is a personality trait! You're comfortable with the uncertainty, and accommodate it in your use and expectations. You're left feeling good about the experience, within that uncertainty. Others are repelled by uncertainty, so will have a negative experience, regardless of how well it may work for a subset of tasks they try, because that repulsive uncertainty is always present. I think it would be interesting (and possibly very useful/profitable for the marketing/UI departments of companies that use AI) to find the relation between perceived AI usefulness and the results of some of the "standard" personality tests.

kenjackson 1y ago
I wonder if this is like dishwasher usage. As a kid growing up we never used the dishwasher. It was just the drying rack. The reason was you had to rinse off the big stuff anyways, and then the resulting quality of dishwashing was poor in it. You'd often get a fork with rice stuck between it still, which was unacceptable. As a grown up now I use a dishwasher for everything that is permitted to go in it. I still have to rinse off plates first, and occasionally I do see rice between a fork that I have to then clean manually. But I'm not comfortable knowing that it won't clean as well as I could by hand, but it does a good enough job -- and in some ways a much better job (it uses much hotter water than I do by hand). I don't know if my mom could ever really be comfortable with it though.
- bluefirebrand 1y ago
  Imagine if the advice for Dishwasher usage mirrored the advice for AI "You have to iterate on the output to get good results" Just keep running that dishwasher until they're clean! If you run it and they're still dirty, load it up with soap and try again!
  - keybrd-intrrpt 1y ago
    That's all new technology though. Dishwashers _were_ like that. What's seemed to change are people's expectations of technology that "just works". When in reality, we are in the infant years of AI/ML and LLMs We're so spoiled by the pace of innovation we're upset it requires a bit of hand-holding while they figure things out.

leptons 1y ago

It's fine if LLMs are used casually, for things that don't affect anyone but the user. But when someone plugs an LLM into Social Security or other governmental bodies to take action on real human beings, then disaster awaits. Nobody is going to care if the LLM got it wrong if you're just chatting with it or writing some wonky code that doesn't matter in the real world, but when your government check is reduced or deleted by an LLM that is hallucinating, then the real problems start. These things should not be trusted with anything but the least consequential actions an individual would use it for.

gte525u 1y ago
^This - we're trying to use one to partially automate some system engineering type activities. It's great for reviews where any given reviewer could be expected to have a misunderstanding of certain details or skip a section (RAG somewhat helps this) - but it's frustrating for artifact generation where missing details cascade through the project. As great as the technology (right now) it seems so far from reliable business process automation.

light_hue_1 1y ago

> It’s mostly right enough What do you use it for? In my space, "mostly right enough" isn't useful. Particularly when that means that the errors are subtle and I might miss them. I can't write whitepapers that tell people to do things that would result in major losses.

fellowniusmonk 1y ago

I really don't understand people who are down on LLM. In terms of code output. I have gone from the productivity of being a Sr. Engineer to a team with .8 of a Sr. Engineer, 5 Jr. Engineers and One dude solely dedicated to reading/creating documentation. Unlike a lot of my fellow engineers who are also from traditional CS backgrounds and haven't worked in revenue restricted startup environments, I also have been VERY into interpreted languages like ruby in the past. Now compiled languages are even better, I think from a velocity perspective compiled languages are now incredibly on par for prototyping velocity and have had their last weakness removed. It's both exciting and scary, I can't believe how people are still sleep walking in this environment and don't realize we are in a different world. Once again the human inability to "gut reason" about exponentials is going to screw us all over. One terribly overlooked thing I've noticed that I think explains the differing takes. Foundation of my position here: https://www.nature.com/articles/s41598-020-60661-8 https://www.nature.com/articles/s41598-020-60661-8 Within the population that writes code there are a small number of successful people who approach the topic in a ~purely mathematical approach, and a small number of successful people that approach writing code in a ~purely linguistic approach. Most people fall somewhere in the middle. Those who are on the MOST extreme end of the mathematic side and are linguistically bereft HATE LLM's and effectively cannot use them. My guess is that HN population will tend to show stronger reactions against LLM's because it was heavily seeded with functional programmers which I think has a concentration of the successful extremely math focused. I worked for several years in a purely functional shop and that was my observation: Elixir, Haskell, Ramda. Just my speculation.

whyowhy3484939 1y ago
There is this interesting thing called the Paradox of Automation where increasing automation increases the importance of human intervention. We are trying this out on a societal level. It will be.. interesting, to say the least. Also, congratulations on becoming a team. I sure hope you have the mental bandwidth to check all that output carefully. If so, doubly congrats, because you might be the smartest human that ever lived.
- fellowniusmonk 1y ago

foobiekr 1y ago

Charitably, your low expectations are probably the source of your finding them acceptable. It’s also possible - and you should not take this as an insult, it’s just the way it is - you may not know enough about the subjects of your interactions to really spot how wrong they are. However the cases you list - brainstorming - don’t really care about wrong answers. Coding is in the eye of the beholder, but for anything that isn’t junk glue code, scripts or low-complexity web stuff, I find the output of LLMs just short of horrendous.

CuriouslyC 1y ago
The code that the best frontier models produce is definitely good if you prompt it with what you believe "good" means, with the caveat that code quality depends heavily on the language -- Python, Typescript/Javascript, Java and C are quite good, Rust, C++ and Go tend to be decent to weak depending on the specific model, and other languages are poor.
- fellowniusmonk 1y ago
  Yeah, I've had mixed results with Rust. Oddly it's been most helpful for me so far in getting Rust code running in WASM without having to know anything about WASM, which I have found delightful.
- foobiekr 1y ago
  The C output is absolutely terrible. I cannot fathom an experienced C coder who has found otherwise for anything non trivial. The code is full of things like return from stack, poor buffer size discipline, etc.